中文摘要：

行人检测是视频大数据中提取信息的关键技术之一,是视频大数据挖掘的关键环节。提出了一种基于稀疏多尺度分割和级联形变模型的行人检测算法。首先设计基于图像纹理的稀疏多尺度分割算法提取潜在行人区域,完成初级多尺度检测;同时缩小检测范围,剔除大量背景区域;再基于级联形变模型在候选特征区域进行精细检测,最终实现由粗到细的快速行人检测。在TUD-Crossing和TUD-Pedestrian等公开数据集上对算法进行了测试。实验结果表明,本文算法降低了虚警率,提升了检测速度。

英文摘要：

Pedestrian detection is one of the key technologies in the large video data to extract information,which is an important link in the process of large video data mining.This is a difficult problem because pedestrian can vary from place to place and time to time.The changes in illumination and viewpoint,variability in shape,non-rigid deformations all can cause variations.In order to achieve a fast and robust pedestrian detection,this paper proposes a pedestrian detection algorithm based on sparse multi-scale image segmentation and cascade deformable part model.Through the sparse multiscale image segmentation algorithm based on texture,lots of background region is eliminated and the interesting area is extracted.In the segmented interesting area,ageneral method is used for building cascade classifiers from part-based deformable models such as pictorial structures.Pictorial structures describe objects by a collection of parts included in a deformable configuration.Each part stands for local appearance properties of a part of the body while the deformable configuration is presented by spring-like connections between parts.The model focuses primarily on the case of star-structured models and show how a simple algorithm based on partial hypothesis pruning can speed up object detection.A discriminative procedure called Latent SVM is used to train these models.Lots of experiments are conducted on public data sets TUD-Crossing and TUD-Pedestrian.Experimental results show that little detection accuracy is increased by our algorithm,and the detection speed is improved obviously.